Iris Diana Flores

[irisdiana00]

    {
        "preferred_name": "Iris",
        "csulb_email": "iris.flores02@student.csulb.edu",
        "pronouns": "she/her",
        "previous_gis_experience": " Undergraduate GIS classes, GIS minor, So Cal Gas gis researcher ",
        "previous_programming_experience": "MSGIS CSULB intro to python, and Python with ARCpy",
        "previous_remotesensing_experience": " Undergraduate Environmental Remote Sensing, Summer Research Project such as Satellite imagery processing techniques utilized Google Earth Engine to apply machine learning algorithm for the detect mangrove forest change around the Red Sea ",
        "github_repo_link": "https://github.com/irisdiana00/GISC606",
        "github_project_link": "https://github.com/users/irisdiana00/projects/4",
        "what_you_hope_to_get_out_of_this_class": "Lidar and Multispectral image processing and classification methods",
    }

[alex-pakalniskis]

Hey @irisdiana00 ,

Thanks for sharing about yourself. Could you also share about your larger MS project taking place outside this class?

I'd like to curate the course content to the interests of the students (including you), so anything you can share about your project or interests in remote sensing will help me. It sounds like you're most interested in content related to remote sensing data and automating processing/analysis with code?

Along those lines, I'd like you to start thinking about your final project for this class. I'm encouraging all students to pick a topic related to their larger MS project, but you are free to select any apply remote sensing topic of interest.

Look forward to working with you this semester.

Best, Alex

[irisdiana00]

Hi @alex-pakalniskis, The main goal of the project is to create a 3D visualization of an unsurveyed area with dense tree cover which also contains data on tree species and health. To do so, LiDAR will be used to create a 3D model of the area and analyze the polygon shapes of the tree or plant species to make species identifications and give measurements for tree trunk size. Concurrently, drone imagery will be used to create a 3D point cloud image. Spectral data from the drone imagery will also be used to identify unique plant species and polygon analysis will also be done to give identifications to tree species.

I understand a few concepts but I'm most definitely no expert, yet. What I know is that in order to classify trees we'll need to make sure our lidar has the classifications. From what I have read in previous literature, most researchers use deep learning models. So we can use all the help, suggestions, feedback we can get.

Thank You, Iris

[irisdiana00]

GISC 606 Lab01 https://github.com/irisdiana00/GISC606/blob/main/GISC606_Lab1.ipynb

[irisdiana00]

GIS606 Lab02 https://github.com/irisdiana00/GISC606/blob/main/IDF_GISC606_L2.ipynb

[irisdiana00]

LAB 1 Article Summaries https://github.com/alex-pakalniskis/gisc606-spring2023/pull/42 { "your_name": "Iris Flores", "your_csulb_email": "iris.flores02@csulb.edu", "articles": [ { "title": "Mapping Burns and Natural Reforestation using Thematic Mapper Data", "authors": "Lopez Garcia, M., and V. Caselles", "link": "https://www.researchgate.net/publication/246761915_Mapping_burns_and_natural_reforestation_using_Thematic_Mapper_data", "your_summary": "Garcia M. & Casellas V. (2008) research studies areas that have been affected by forest fires in the province of Valencia, Spain. In addition, the research focuses on monitoring vegetation regeneration on the areas affected by using remote sensing applications. Mapping of the burned areas have been acquired by using Landsat 5 Thematic Mapper. Their analysis involves using spectral signatures and vegetation indexes that is best suited for the study site. Through statical analysis, they determine that the best VI to both map affected areas and monitor them include the use of Bands TM4 (0.76-0.90) and TM7(2.08-2.35) spectral ranges." }, { "title": "Landscape Assessment: Remote Sensing of Severity, the Normalized Burn Ratio; and Ground Measure of Severity, the Composite Burn Index.", "authors": "Key, C. and N. Benson, N", "link": "https://www.researchgate.net/publication/241687027_Landscape_Assessment_Ground_measure_of_severity_the_Composite_Burn_Index_and_Remote_sensing_of_severity_the_Normalized_Burn_Ratio", "your_summary": "In the research article Landscape Assessment by Key C.H & Bensom N.C (2016),, focus on the need to address landscape assessment over larger areas. They used remotes sensing techniques to design applicable methods designed to study large areas that have been affected by fires. Their research integrates two methodologies; the Normalized Burn Ratio acquired from pre- and postfire datasets and the used of Burn Remote Sensing (BR) determined from 30-meter data acquired from Landsat. The normalized burn ratio (NBR) is another vegetation index that allows you to visualize burned areas to understand fire severity. NBR = (NIR-SWIR)/(NIR + SWIR) NBR to understand wildfire, you can perform the calculation on a remotely sensed image taken before to the fire and on another after, then calculate the difference between the two. This difference image is called a dNBR. Using these datatypes from two timeframes Key C.H & Bensom N.C (2016) determine wildfire change detection effects over large areas. " } ]

[irisdiana00]

LAB 2 Article Summaries { "your_name": "Iris Diana Flores", "your_csulb_email": "iris.flores02@student.csulb.edu", "articles": [ { "title": "Using the satellite-derived NDVI to assess ecological responses to environmental change", "authors": "Nathalie Pettorelli1,Jon Olav Vik1, Atle Mysterud1,Jean-Michel Gaillard2, Compton J. Tucker3 and Nils Chr. Stenseth1", "link": " http://www.arctic-predators.uit.no/biblio_IPYappl/PettorelliTREE05%20ecological%20responses%20NDVI.pdf ", "your_summary": "In the research article “Using the satellite derived NDVI to assess ecological responses to environmental change” by Pettorelli et al. (2005) reviews remote sensing applications use when it comes to dealing with the warming climate and its effects on Earth biodiversity and ecosystems. The use of The Normalized Difference Vegetation Index (NDVI) has emerged as a popular technique to study ecosystems and their direct/indirect effects of environmental change. Furthermore Pettorelli et al. reviews the research where NDVI has been used to research spatial and temporal trends, to identify vegetation and population, diversity, and patterns in this changing climate." }, { "title": "EarthPy: A Python package that makes it easier to explore and plot raster and vector data using open source Python tools.", "authors": "Leah Wasser1, Maxwell B. Joseph1, Joe McGlinchy1, Jenny Palomino1,Korinek, Nathan1, Chris Holdgraf2, and Tim Head3", "link": " https://joss.theoj.org/papers/10.21105/joss.01886.pdf ", "your_summary": " EarthPy is a tool that allows for the integration of spatial data. EarthPy is a python package for the purpose of making it easier for users to explore and plot raster and vector data in open-source applications. The use of Earthpy depends on geopandas that focuses on vector dataset ansksysis/ processing and rasterio that enable the input/processing/output of raster files as numpy arrays.." }, { "title": "Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021.", "authors": "Xu Y, Yang Y, Chen X, Liu Y. ", "link": " https://joss.theoj.org/papers/10.21105/joss.01886.pdf ", "your_summary": "In the research article “Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021” by Yang et al. (2002) use statistical methods and scientific mapping find and analyze the literature behind the used of the Normalized vegetation indexes in vegetation research. The analysis was conducting using the bibliometric applications such as R package and biblio-shiny to analyze/convert quantitative data. According to the bibliometric analysis Yang et al. (2002) identify a exponential increase of NDVI application to various research field this correlated to the rise of remotes sensing applications and platforms.." } ] }\

[irisdiana00]

https://github.com/irisdiana00/GISC606/blob/main/GISC606_L3.ipynb

[irisdiana00]

https://github.com/irisdiana00/GISC606/blob/main/GISC606_L4.ipynb

[irisdiana00]

https://irisdiana00.github.io/lab5/

[irisdiana00]

https://github.com/irisdiana00/Final_Lab